1. Field of the Invention
The present invention relates to systems and methods for information analysis, and more particularly to a system for online consumer credit reporting information analysis and distribution.
2. Background
Data accuracy is dependent upon the accuracy of data sources. Inaccurate consumer financial data has become an enormous problem for consumers, industry, and government. For consumers, inaccurate data, data theft, and other data abuse misrepresent a consumer's identity. Correcting inaccurate data and recovering from data abuse entails a tremendous amount of effort.
Three major credit reporting bureaus, namely, TransUnion™, Experian™, and Equifax™ maintain and report credit information for consumers. The credit reporting industry seeks to report as much credit information as possible to subscribers, which can be financial institutions, employers, landlords, retail environments, government entities, and any other entities authorized to purchase credit data access from credit reporting bureaus.
Major problems exist with the information maintained by credit reporting bureaus. The sources of creditor information are not entirely reliable, there may be countless differences in a consumer's specific data that could be erroneous, and the vast amount of information received by the credit reporting bureaus makes detecting, verifying, and correcting errors to be an enormous task. Indeed, the most cost-effective way for a credit reporting bureau to handle errors is to rely on the consumer to detect, verify, and seek correction of errors.
Currently there exist over 2.36 billion errors on credit reports for approximately 133 million credit report holders in the U.S. Additionally, there are 27,123 identity thefts daily, amounting to over 9.9 million victims per year in the U.S. An estimated twenty-nine percent (29%) of credit reports are believed to contain serious errors, such as false delinquencies or accounts that do not belong to the consumer. Such errors could result in creditors denying credit to a consumer. If credit is approved despite such credit report errors, a creditor is likely to charge a consumer a higher rate of interest than the rate of interest available if the consumer's credit report lacked erroneous information.
Around forty-one percent (41%) of credit reports are believed to contain personal demographic identifying information that is misspelled, outdated, belonging to a different person, or otherwise incorrect. Approximately twenty-six percent (26%) of credit reports are believed to list credit accounts that have been closed by the consumer but incorrectly remain listed as open accounts.
Besides erroneous reporting of negative information, errors are also made by omission of positive information. Approximately twenty percent (20%) of credit reports are believed to be missing major credit, loan, mortgage, or other consumer accounts that would demonstrate creditworthiness of a consumer.
To attempt to correct reporting errors, the credit repair industry offers a manual system for correcting data that is reported by credit reporting bureaus. The credit repair industry operates as follows: consumers are charged large up-front fees along with monthly recurring fees; credit repairs starts by mailing requests for hard copies of the consumer's credit reports; the consumer waits for the return mail delivery of these hard copies of the credit reports to the credit repair company; the credit repair company begins to manually process any disputes and claims without receiving directions from the consumer; finally, disputes and claims are mailed by the credit repair company to the credit reporting bureaus for processing.
Credit reporting bureaus allow for manual entering of any statements, regarding credit items, which the consumer believes are in error. Such statements are reported in the credit reports available online through the credit reporting bureaus' systems. However, the process available directly from the credit reporting bureaus is a lengthy process. For example, a credit reporting bureau's online system requires that the consumer provide identifying information and contact information (such as address, telephone number, social security number, birth date, and the like) each time entering a statement. This conventional process is even more burdensome in that such identifying information and contact information must be separately provided for each credit reporting bureau system. Consequently, a need exists for a method to enable consumers to provide information in a more uniform manner.
Credit scores are often calculated using predictive modeling. Sometimes called “credit risk scores”, the credit scores are used as predictive tools to assess consumer credit and bankruptcy risk in order for credit grantors to make profitable decisions in granting credit such as, customer acquisition (prescreening and marketing), credit origination and underwriting, and customer management.
To predict credit risk, credit grantors often use a credit score. Such credit scores are widely used by credit grantors in the United States, the United Kingdom, Canada, and South Africa. Credit grantors in any country that has credit reporting bureau data are increasingly using credit scores.
Besides credit risk scores, other scores used include revenue and attrition scores, application fraud scores, credit-based insurance scores, small business risk scores, collections and recovery scores, and marketing scores.
The leading producer for credit scores and models for predicting consumer behavior is the Fair Isaac Credit Organization. The firm's FICO™ score is the most widely used credit score in the credit reporting industry. The FICO™ score (a number between 300 and 850) is calculated with a proprietary model. The proprietary model evaluates the information in a consumer's credit report and compares the information with millions of other credit reports. The higher the score, the more likely a consumer is to be approved for the granting of credit and to receive favorable interest rates.
While one is not able to identically duplicate the calculation of a FICO™ score, one may generally predict qualitative results from negative aspects of a consumer's credit data (such as late payments, bankruptcies, and court judgments) and from positive aspects of a consumer's credit data (such as low debt-to-income ratio, no late payments, and a lengthy, successful credit history).
Currently, no solution exists for providing a tool or calculator that takes data, categorizes the data, displays the data with categories, and calculates related interest rates and payment schedules. Additionally, no solution exists that may simultaneously display any combination of a current credit score, an estimated credit score, and a scalable predicted future credit score.
Identity theft is the fastest growing and number one reported crime in the United States today. Generally, a victim of identity theft has few effective options to correct the situation. Understanding what needs to be done and scheduling time to correct the situation can be overwhelming to most consumers. A consumer generally becomes a victim of identity theft because the consumer's personal information is not properly protected by current creditors, past creditors, past potential creditors, credit reporting bureaus, financial institutions, retail environments, government entities, and any other entities likely to possess personal information.
No identity theft protection industry exists, except for credit report monitoring services, which simply provide a consumer a periodic copy of a credit report. There is no automated solution to address the problem or a method of informing the appropriate authorities for assistance.
Therefore, what is desired is a system and method for correcting credit reporting errors (such as inaccurate, outdated, or unverifiable information), protect against identity theft, and predict future credit scores.